Rolling bearings fault diagnosis based on two-stage signal fusion and deep multi-scale multi-sensor network.

In order to realize high-precision diagnosis of bearings faults in a multi-sensor detection environment, a fault diagnosis method based on two-stage signal fusion and deep multi-scale multi-sensor networks is proposed. Firstly, the signals are decomposed and fused using weighted empirical wavelet transform to enhance weak features and reduce noise. Secondly, an improved random weighting algorithm is proposed to perform a second weighted fusion of the signals to reduce the total mean square error.

In-Sensor Tactile Fusion and Logic for Accurate Intention Recognition.

Touch control intention recognition is an important direction for the future development of human-machine interactions (HMIs). However, the implementation of parallel-sensing functional modules generally requires a combination of different logical blocks and control circuits, which results in regional redundancy, redundant data, and low efficiency. Here, a location-and-pressure intelligent tactile sensor (LPI tactile sensor) unprecedentedly combined with sensing, computing, and logic is proposed, enabling efficient and ultrahigh-resolution action-intention interaction.

Recent advances in flexible memristors for advanced computing and sensing.

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption.

A new Mach-Zehnder interference temperature measuring sensor based on silica-based chip.

A new type of silicon-based Mach-Zehnder interference (MZI) temperature sensor chip with "mosquito coil" structure was designed. The sensor chip used a new MZI interference structure. After the light entered the chip, it split and interfered in the combiner of the chip.

Bionanotechnology and bioMEMS (BNM): state-of-the-art applications, opportunities, and challenges.

The development of micro- and nanotechnology for biomedical applications has defined the cutting edge of medical technology for over three decades, as advancements in fabrication technology developed originally in the semiconductor industry have been applied to solving ever-more complex problems in medicine and biology. These technologies are ideally suited to interfacing with life sciences, since they are on the scale lengths as cells (microns) and biomacromolecules (nanometers). In this paper, we review the state of the art in bionanotechnology and bioMEMS (collectively BNM), including developments and challenges in the areas of BNM, such as microfluidic organ-on-chip devices, oral drug delivery, emerging technologies for managing infectious diseases, 3D printed microfluidic devices, AC electrokinetics, flexible MEMS devices, implantable microdevices, paper-based microfluidic platforms for cellular analysis, and wearable sensors for point-of-care testing.

Effect of Time-Varying Multiplicative Noise on DNN- k WTA Model.

Among many k -winners-take-all ( k WTA) models, the dual-neural network (DNN- k WTA) model is with significantly less number of connections. However, for analog realization, noise is inevitable and affects the operational correctness of the k WTA process. Most existing results focus on the effect of additive noise.

Conformal Human-Machine Integration Using Highly Bending-Insensitive, Unpixelated, and Waterproof Epidermal Electronics Toward Metaverse.

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals, which is critical to the breakthrough development of metaverse. Interactive electronics face common dilemmas, which realize high-precision and stable touch detection but are rigid, bulky, and thick or achieve high flexibility to wear but lose precision. Here, we construct highly bending-insensitive, unpixelated, and waterproof epidermal interfaces (BUW epidermal interfaces) and demonstrate their interactive applications of conformal human-machine integration.

A flexible optoacoustic blood 'stethoscope' for noninvasive multiparametric cardiovascular monitoring.

Quantitative and multiparametric blood analysis is of great clinical importance in cardiovascular disease diagnosis. Although there are various methods to extract blood information, they often require invasive procedures, lack continuity, involve bulky instruments, or have complicated testing procedures. Flexible sensors can realize on-skin assessment of several vital signals, but generally exhibit limited function to monitor blood characteristics.

Analysis on the inherent noise tolerance of feedforward network and one noise-resilient structure.

In the past few decades, feedforward neural networks have gained much attraction in their hardware implementations. However, when we realize a neural network in analog circuits, the circuit-based model is sensitive to hardware nonidealities. The nonidealities, such as random offset voltage drifts and thermal noise, may lead to variation in hidden neurons and further affect neural behaviors.

Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging deep CNN prior.

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models.

Technology Roadmap for Flexible Sensors.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited.

Higher-Order Topological States in Thermal Diffusion.

Unlike conventional topological materials that carry topological states at their boundaries, higher-order topological materials are able to support topological states at boundaries of boundaries, such as corners and hinges. While band topology has been recently extended into thermal diffusion for thermal metamaterials, its realization is limited to a 1D thermal lattice, lacking access to the higher-order topology. In this work, the experimental realization is reported of a higher-order thermal topological insulator in a generalized 2D diffusion lattice.

Flexible integrated sensor with asymmetric structure for simultaneously 3D tactile and thermal sensing.

The human body detects tactile stimuli through a combination of pressure force and temperature signals via various cutaneous receptors. The development of a multifunctional artificial tactile perception system has potential benefits for future robotic technologies, human-machine interfaces, artificial intelligence, and health monitoring devices. However, constructing systems beyond simple pressure sensing capabilities remains challenging.

Extracellular-matrix-mimicked 3D nanofiber and hydrogel interpenetrated wound dressing with a dynamic autoimmune-derived healing regulation ability based on wound exudate.

Dynamic regulation of wound physiological signals is the basis of wound healing. Conventional biomaterials delivering growth factors to drive wound healing leads to the passive repair of soft tissues because of the mismatch of wound healing stages. Meanwhile, the bioactivity of wound exudate is often restricted by oxidation and bacterial contamination.

A Review of Machine Learning for Near-Infrared Spectroscopy.

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided.

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring.

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes.

A Low-Voltage and Power-Efficient Capless LDO Based on the Biaxially Driven Power Transistor Technique for Respiration Monitoring System.

In this study, a 0.8-V- V 200-mA- I capless low-dropout voltage regulator (LDO) is developed for a wireless respiration monitoring system. The biaxially driven power transistor (BDP) technique is proposed in the LDO, with a current driven stimulation on the bulk and a voltage on the gate terminal.

Freestanding and Scalable Force-Softness Bimodal Sensor Arrays for Haptic Body-Feature Identification.

Tactile technologies that can identify human body features are valuable in clinical diagnosis and human-machine interactions. Previously, cutting-edge tactile platforms have been able to identify structured non-living objects; however, identification of human body features remains challenging mainly because of the irregular contour and heterogeneous spatial distribution of softness. Here, freestanding and scalable tactile platforms of force-softness bimodal sensor arrays are developed, enabling tactile gloves to identify body features using machine-learning methods.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration.

Wavefront shaping: A versatile tool to conquer multiple scattering in multidisciplinary fields.

Optical techniques offer a wide variety of applications as light-matter interactions provide extremely sensitive mechanisms to probe or treat target media. Most of these implementations rely on the usage of ballistic or quasi-ballistic photons to achieve high spatial resolution. However, the inherent scattering nature of light in biological tissues or tissue-like scattering media constitutes a critical obstacle that has restricted the penetration depth of non-scattered photons and hence limited the implementation of most optical techniques for wider applications.

Deep and Domain Transfer Learning Aided Photoacoustic Microscopy: Acoustic Resolution to Optical Resolution.

Acoustic resolution photoacoustic micros- copy (AR-PAM) can achieve deeper imaging depth in biological tissue, with the sacrifice of imaging resolution compared with optical resolution photoacoustic microscopy (OR-PAM). Here we aim to enhance the AR-PAM image quality towards OR-PAM image, which specifically includes the enhancement of imaging resolution, restoration of micro-vasculatures, and reduction of artifacts. To address this issue, a network (MultiResU-Net) is first trained as generative model with simulated AR-OR image pairs, which are synthesized with physical transducer model.

Speckle-Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning.

Face recognition has become ubiquitous for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data and should be protected. Software-based cryptosystems are widely adopted to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power.

An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements.

Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human-machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world.

A Silicon-Based Adaptable Edge Coherent Radar Platform for Seamless Health Sensing and Cognitive Interactions With Human Subjects.

Empowered by the rapid advancements of semiconductor techniques, emerging areas such as industry 4.0, precise healthcare, pervasive communications, intelligent robots, and smart buildings are to be realized, which put substantial demands on low-power and high-performance cognitive edge sensors. Capabilities of precise sensing and seamless interactions with human subjects are pivotal to boosting versatile Internet of Everything (IoE) applications.

Morphology-dependent resonance enhanced nonlinear photoacoustic effect in nanoparticle suspension: a temporal-spatial model.

The morphology-dependent resonances (MDRs) hotspot, ubiquity formed between the pairs of nanoparticles in close vicinity, has garnered considerable recent attention. By extending this phenomenon to pulse-laser irradiated nanoparticle suspension, we demonstrate that such collective optical/thermal enhancement can give rise to the nonlinear photoacoustic (PA) generation. In this study, a temporal-spatial analytical expression is derived to quantitatively describe the nonlinear PA signal generation from nanoparticles, incorporating the Grüneisen increase at the microscopic individual particle level and MRDs enhancement at the macroscopic suspension level.